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arxiv: 2605.23903 · v1 · pith:OLJDFC2Znew · submitted 2026-05-22 · 💻 cs.CV

Geo-Align: Video Generation Alignment via Metric Geometry Reward

Zizun Li , Haoyu Guo , Runzhe Teng , Chunhua Shen , Tong He This is my paper

Pith reviewed 2026-05-25 04:14 UTC · model grok-4.3

classification 💻 cs.CV
keywords video generationcamera controlreinforcement learning3D geometryreward mechanismvideo re-renderingtrajectory alignment
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0 comments X

The pith

A reinforcement learning framework uses metric 3D trajectory extraction to align camera paths in generated videos without paired real data.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that reinforcement learning can refine camera-controlled video re-rendering by adding a reward that penalizes mismatches between target and generated camera trajectories. It builds this reward from a metric 3D estimator applied directly to the output videos and pairs real conditioning videos with synthetic target trajectories to avoid needing synchronized multi-view training pairs. A sympathetic reader would care because existing supervised fine-tuning approaches trained on synthetic data generalize poorly to real videos and fail to maintain accurate physical scales and paths.

Core claim

Geo-Align is the first reinforcement learning framework for camera-controlled video re-rendering. Built on a pretrained model, it optimizes via a scale-aware perceptual reward that deploys a metric 3D estimator to extract camera trajectories from generated videos and explicitly penalizes deviations in rotation and translation. A data pipeline that combines real-world conditioning videos with target trajectories from synthetic data removes dependence on paired training examples. Experiments show the resulting model exceeds supervised learning baselines on both precise camera controllability and visual fidelity.

What carries the argument

The metric 3D estimator that pulls precise camera trajectories out of generated videos to form a penalizing reward on rotation and translation errors.

If this is right

  • Camera trajectories in generated videos adhere more closely to targets than those from supervised fine-tuning on synthetic data.
  • Visual quality improves at the same time as geometric accuracy without requiring additional paired real-world training data.
  • The model handles out-of-distribution real-world conditioning videos more reliably than prior supervised approaches.
  • The same reward construction can be applied on top of any existing pretrained video generation model.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same trajectory-based reward could be adapted to enforce consistency on other geometric properties such as object sizes or scene layout.
  • Replacing the reinforcement learning loop with direct regression on the extracted trajectories might simplify training while retaining the geometric signal.
  • The synthetic-target plus real-conditioning pipeline offers a template for incorporating geometric constraints into other video synthesis tasks.

Load-bearing premise

A metric 3D estimator can reliably extract precise camera trajectories from the generated videos to compute an effective penalizing reward signal.

What would settle it

An independent metric 3D estimator applied to videos produced by the trained model finds no reduction in rotation or translation error relative to supervised baselines.

Figures

Figures reproduced from arXiv: 2605.23903 by Chunhua Shen, Haoyu Guo, Runzhe Teng, Tong He, Zizun Li.

Figure 1
Figure 1. Figure 1: Given a conditioning video, Geo-Align synthesizes a novel view video according to the [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Geo-Align pipeline. Given a conditioning video, we sample a camera trajectory from other camera-annotated data and scale it to a plausible range, with the scaling factor drawn from a truncated Gaussian distribution. After the model generates a set of rollout videos, a metric 3D evaluator assesses the camera trajectory of each sample to compute geometry rewards. Finally, the model is optimized via Group Rel… view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative results on the DAVIS [10] dataset. Geo-Align demonstrates superior capa￾bilities in maintaining geometric consistency between the foreground subject and the background, whereas other methods suffer from varying degrees of distortion [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: More visualization results on CityWalk [11] dataset. For each example, the top row illustrates the input video, whereas the bottom row visualizes our results following the target trajectory. 4.3 Evaluation protocol We follow the evaluation protocol of ReDirector [2], using 50 videos from the DAVIS dataset. By applying 10 ReCamMaster [1] camera trajectories per video, we construct 500 test cases with length… view at source ↗
Figure 5
Figure 5. Figure 5: More qualitative comparison on DAVIS [10] dataset. For each example, the top row illustrates the input video, while the second and third rows present the results of ReDirector [2] and our model, respectively. Input Video Ours [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Failure Case. the model remains susceptible to failure when faced with excessively fast rotations, large translations, or large foreground objects close to the camera. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
read the original abstract

Camera-controlled video generation has achieved remarkable progress in recent years. However, existing video-to-video re-rendering methods primarily rely on Supervised Fine-Tuning using synthetic datasets. At present, there is an extreme scarcity of synchronized, multi-view real-world video data. Consequently, the prevailing paradigm often exhibits limited generalization when processing out-of-distribution real-world videos, with models struggling to accurately adhere to physical scales and camera trajectories. To bridge this gap, we propose Geo-Align, the first Reinforcement Learning framework specifically designed for camera-controlled video re-rendering. Built upon a pretrained model, we optimize the model through a scale-aware perceptual reward mechanism. Specifically, we introduce a metric 3D estimator to extract precise camera trajectories from generated videos, explicitly penalizing deviations in rotation and translation. Furthermore, we meticulously designed a data pipeline strategy based on real-world conditioning videos and target camera trajectories derived from synthetic data, eliminating the reliance on paired data. Extensive experiments demonstrate that Geo-Align consistently outperforms existing supervised learning baselines in both precise camera controllability and visual fidelity, indicating the effectiveness of our method.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper proposes Geo-Align, the first RL framework for camera-controlled video re-rendering built on a pretrained model. It introduces a scale-aware perceptual reward derived from a metric 3D estimator that extracts camera trajectories (rotation/translation) from the generated videos themselves to penalize deviations, combined with a data pipeline using real-world conditioning videos and synthetic target trajectories to avoid paired data. The central claim is that this yields consistent outperformance over supervised fine-tuning baselines in precise camera controllability and visual fidelity.

Significance. If the result holds with validated reward signals, the approach would address the scarcity of synchronized multi-view real-world data by shifting from SFT to RL with geometric rewards, potentially improving generalization to out-of-distribution inputs in video generation tasks.

major comments (2)
  1. [Abstract] Abstract and Experiments section: The claim that 'Geo-Align consistently outperforms existing supervised learning baselines' is asserted without any quantitative metrics, tables of results, ablation studies, or error analysis, rendering the central empirical claim impossible to evaluate.
  2. [Method] Method and Experiments: The reward mechanism depends on the metric 3D estimator remaining accurate and unbiased on imperfect, artifact-containing generated videos during RL training, yet no validation, robustness tests, or comparison of estimator performance on real vs. generated content is reported; any systematic bias would invalidate the controllability gains.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and outline revisions to improve the manuscript's clarity and rigor.

read point-by-point responses

  1. Referee: [Abstract] Abstract and Experiments section: The claim that 'Geo-Align consistently outperforms existing supervised learning baselines' is asserted without any quantitative metrics, tables of results, ablation studies, or error analysis, rendering the central empirical claim impossible to evaluate.

    Authors: We agree that the abstract states the outperformance claim at a high level without supporting numbers. Although the experiments section presents comparative results, we will revise the abstract to include key quantitative metrics (e.g., trajectory error and fidelity scores) and expand the experiments with an explicit error analysis subsection and ablation tables to make the central claim directly evaluable. revision: yes

  2. Referee: [Method] Method and Experiments: The reward mechanism depends on the metric 3D estimator remaining accurate and unbiased on imperfect, artifact-containing generated videos during RL training, yet no validation, robustness tests, or comparison of estimator performance on real vs. generated content is reported; any systematic bias would invalidate the controllability gains.

    Authors: This is a substantive concern regarding potential bias in the reward signal. We will add a new subsection in the experiments validating the 3D estimator's accuracy and bias on generated videos (including comparisons to real videos and tests under artifact conditions) to confirm the reward's reliability. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical claims rest on external estimator and experiments

full rationale

The paper describes an RL optimization loop that applies a pre-existing metric 3D estimator to generated video outputs in order to compute a reward penalizing trajectory deviations. This is a conventional reward-modeling step rather than a self-definitional or fitted-input reduction. No equations or claims in the abstract equate the reported controllability gains to quantities defined by the same fitted parameters or by self-citation chains. The central performance assertions are framed as experimental outcomes, not algebraic identities or renamed inputs. The derivation therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review performed on abstract alone; no concrete free parameters, axioms, or invented entities can be identified. The metric 3D estimator is referenced but its internals and any assumptions are unknown.

pith-pipeline@v0.9.0 · 5725 in / 966 out tokens · 23368 ms · 2026-05-25T04:14:17.675130+00:00 · methodology

discussion (0)

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